Sudden glare from trailing vehicles significantly increases driving safety risks. Existing anti-glare technologies such as electronic, manually-adjusted, and electrochromic rearview mirrors, are expensive and lack effective adaptability in different lighting conditions. To address these issues, our research introduces an intelligent rearview mirror system utilizing novel all-liquid electrochromic technology. This system integrates IoT with ensemble and federated learning within a cloud edge collaboration framework, dynamically controlling voltage to effectively eliminate glare and maintain clear visibility. Utilizing an ensemble learning model, it automatically adjusts mirror transmittance based on light intensity, achieving a low RMSE of 0.109 on the test set. Furthermore, the system leverages federated learning for distributed data training across devices, which enhances privacy and updates the cloud model continuously. Distinct from conventional methods, our experiment utilizes the Schmidt-Clausen and Bindels de Boer 9-point scale with TOPSIS for comprehensive evaluation of rearview mirror glare. Designed to be convenient and costeffective, this system demonstrates how IoT and AI can significantly enhance rearview mirror anti-glare performance.

翻译：后方车辆突然产生的眩光显著增加了驾驶安全风险。现有防眩光技术如电子式、手动调节式和电致变色后视镜，成本高昂且在不同光照条件下缺乏有效适应性。为解决这些问题，本研究引入了一种采用新型全液态电致变色技术的智能后视镜系统。该系统在云边协同框架内整合物联网、集成学习与联邦学习，通过动态控制电压有效消除眩光并保持清晰视野。利用集成学习模型，系统基于光照强度自动调节后视镜透光率，在测试集上实现了低至0.109的均方根误差。此外，系统采用联邦学习进行跨设备分布式数据训练，既增强了隐私保护又持续更新云端模型。与常规方法不同，本实验采用Schmidt-Clausen与Bindels de Boer九点量表结合TOPSIS方法对后视镜眩光进行综合评价。该设计兼具便捷性与成本效益，证明了物联网与人工智能可显著提升后视镜防眩光性能。